Nighttime PM<sub>2.5</sub> Concentration Estimation Based on NPP-VIIRS and Interpretable Machine Learning: The Case of Beijing–Tianjin–Hebei Region
Air pollution and public health issues caused by fine particulate matter (PM<sub>2.5</sub>) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM<sub>2.5</sub> concentrations, these methods are limited by weak nighttime...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10963726/ |
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| Summary: | Air pollution and public health issues caused by fine particulate matter (PM<sub>2.5</sub>) are becoming increasingly severe. Although well-established satellite methods exist for retrieving daytime PM<sub>2.5</sub> concentrations, these methods are limited by weak nighttime light radiation. To resolve these challenges, this study proposed a nighttime PM<sub>2.5</sub> concentration estimation method based on explainable machine learning and low-light data. Owing to the complexity of nighttime light sources, primarily composed of artificial lighting and moonlight, both types of light were considered by simulating lunar irradiance and artificial light radiance. This study utilized nighttime lighting, meteorological, various geospatial auxiliary, simulated nighttime light radiation, and ground-based PM<sub>2.5</sub> monitoring data to construct a dataset with an effective sample size of 24,311. A deep neural network model was trained to estimate nighttime PM<sub>2.5</sub> concentrations. The experimental results show that, after adding the simulated nighttime light radiation, the tenfold cross-validation <italic>R</italic><sup>2</sup> of the model improved from 0.6 to 0.73. In addition, 74% of site-based tenfold cross-validation <italic>R</italic><sup>2</sup> values exceeded 0.7, indicating the model's robust spatial adaptability. The model was then used to estimate nighttime PM<sub>2.5</sub> concentrations in the study area for 2021. The Shapley additive explanation model was applied to analyze the effect curves of different predictors on nighttime PM<sub>2.5</sub> to examine the contributions of various factors. This study can serve as a reference for similar research in the future, and the proposed retrieval method offers a broad coverage of nighttime PM<sub>2.5</sub> data, providing a useful supplement to ground station measurements. |
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| ISSN: | 1939-1404 2151-1535 |